




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_						 =					logging.get_logger(__name__)
class 		_A   (    _lowerCamelCase ):
  _UpperCamelCase  :     int					=     ['''input_features''']






  def __init__(    self						:      Dict ,		_A						:      List[Any]=80 ,		_A						:      Dict=16_000 ,		_A						:      List[str]=160 ,		_A						:      List[str]=30 ,		_A						:      Dict=400 ,		_A						:      int=0.0 ,		_A						:      Optional[Any]=False ,		**_A						:      Tuple ,		)						->       int:






       """simple docstring"""







       super().__init__(
           feature_size=_A ,		sampling_rate=_A ,		padding_value=_A ,		return_attention_mask=_A ,		**_A ,		)
       lowercase   :  List[str]        =					n_fft
       lowercase   :  List[Any]        =					hop_length
       lowercase   :  List[str]        =					chunk_length
       lowercase   :  Union[str, Any]        =					chunk_length * sampling_rate
       lowercase   :  Optional[int]        =					self.n_samples // hop_length
       lowercase   :  Optional[int]        =					sampling_rate
       lowercase   :  Optional[int]        =					mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2 ,		num_mel_filters=_A ,		min_frequency=0.0 ,		max_frequency=8_000.0 ,		sampling_rate=_A ,		norm='''slaney''' ,		mel_scale='''slaney''' ,		)






  def    __a     (    self						:      Tuple ,		_A						:      Optional[int] )						->       np.ndarray:






       """simple docstring"""







       lowercase   :  Optional[int]        =					spectrogram(
           _A ,		window_function(self.n_fft ,		'''hann''' ) ,		frame_length=self.n_fft ,		hop_length=self.hop_length ,		power=2.0 ,		mel_filters=self.mel_filters ,		log_mel='''log10''' ,		)
       lowercase   :  Optional[Any]        =					log_spec[:, :-1]
       lowercase   :  List[Any]        =					np.maximum(_A ,		log_spec.max() - 8.0 )
       lowercase   :  List[Any]        =					(log_spec + 4.0) / 4.0
       return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def    __a     (    _A						:      Optional[int] ,		_A						:      int ,		_A						:      Optional[int] = 0.0 )						->       List[np.ndarray]:






       """simple docstring"""







       if attention_mask is not None:
            lowercase   :  Union[str, Any]        =					np.array(_A ,		np.intaa )
            lowercase   :  Optional[int]        =					[]

            for vector, length in zip(_A ,		attention_mask.sum(-1 ) ):
                 lowercase   :  Dict        =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
                 if length < normed_slice.shape[0]:
                      lowercase   :  int        =					padding_value

                 normed_input_values.append(_A )
       else:
            lowercase   :  str        =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

       return normed_input_values






  def __call__(    self						:      List[str] ,		_A						:      Optional[Any] ,		_A						:      Optional[Any] = True ,		_A						:      Tuple = None ,		_A						:      Optional[int] = None ,		_A						:      Tuple = None ,		_A						:      Tuple = "max_length" ,		_A						:      Union[str, Any] = None ,		_A						:      Dict = None ,		_A						:      Union[str, Any] = None ,		**_A						:      Tuple ,		)						->       BatchFeature:






       """simple docstring"""







       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                     f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                     f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
       else:
            logger.warning(
                '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                '''Failing to do so can result in silent errors that might be hard to debug.''' )

       lowercase   :  List[str]        =					isinstance(_A ,		np.ndarray ) and len(raw_speech.shape ) > 1
       if is_batched_numpy and len(raw_speech.shape ) > 2:
            raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
       lowercase   :  int        =					is_batched_numpy or (
           isinstance(_A ,		(list, tuple) ) and (isinstance(raw_speech[0] ,		(np.ndarray, tuple, list) ))
       )

       if is_batched:
            lowercase   :  List[str]        =					[np.asarray([speech] ,		dtype=np.floataa ).T for speech in raw_speech]
       elif not is_batched and not isinstance(_A ,		np.ndarray ):
            lowercase   :  Dict        =					np.asarray(_A ,		dtype=np.floataa )
       elif isinstance(_A ,		np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
            lowercase   :  Optional[Any]        =					raw_speech.astype(np.floataa )

       # always return batch
       if not is_batched:
            lowercase   :  str        =					[np.asarray([raw_speech] ).T]

       lowercase   :  Union[str, Any]        =					BatchFeature({'''input_features''': raw_speech} )

       # convert into correct format for padding

       lowercase   :  Union[str, Any]        =					self.pad(
           _A ,		padding=_A ,		max_length=max_length if max_length else self.n_samples ,		truncation=_A ,		pad_to_multiple_of=_A ,		return_attention_mask=return_attention_mask or do_normalize ,		)

       # zero-mean and unit-variance normalization
       if do_normalize:
            lowercase   :  int        =					self.zero_mean_unit_var_norm(
                padded_inputs['''input_features'''] ,		attention_mask=padded_inputs['''attention_mask'''] ,		padding_value=self.padding_value ,		)
            lowercase   :  List[str]        =					np.stack(padded_inputs['''input_features'''] ,		axis=0 )

       # make sure list is in array format
       lowercase   :  int        =					padded_inputs.get('''input_features''' ).transpose(2 ,		0 ,		1 )

       lowercase   :  List[Any]        =					[self._np_extract_fbank_features(_A ) for waveform in input_features[0]]

       if isinstance(input_features[0] ,		_A ):
            lowercase   :  Tuple        =					[np.asarray(_A ,		dtype=np.floataa ) for feature in input_features]
       else:
            lowercase   :  Optional[int]        =					input_features

       if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            lowercase   :  Dict        =					padded_inputs['''attention_mask'''][:, :: self.hop_length]

       if return_tensors is not None:
            lowercase   :  Union[str, Any]        =					padded_inputs.convert_to_tensors(_A )

       return padded_inputs






  def    __a     (    self						:      Optional[Any] )						->       Dict[str, Any]:






       """simple docstring"""







       lowercase   :  List[Any]        =					copy.deepcopy(self.__dict__ )
       lowercase   :  Tuple        =					self.__class__.__name__
       if "mel_filters" in output:
            del output["mel_filters"]
       return output





'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



